Cyber security issues are always worthy of attention. Intrusion detection system (IDS) is one of approaches used to protect computer system and identify potential attack. However, existing methods are limited by high-dimensional computational complexity in rare or unknown attack detection task. To improve the ability of detecting anomaly intrusions, a hybrid intrusion detection framework is proposed in this paper. The proposed RuleRCD algorithm first uses fuzzy association rules based on Apriori and K-Means algorithm for normal pattern and major attack detection. The other data are then fed to an active learning-based rare category detection algorithm to identify its attack pattern. This paper also introduces an interactive visualization system, which integrates experts’ decision into intrusion detection workflow. The method improves the effectiveness and interpretability of detection process. KDD-99 dataset is used to evaluate the proposed framework. The result shows that the approach outperforms some methods, especially in terms of the rare attack.
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Supported by the National Natural Science Foundation of China under Grant No. 61100053, and SJTU-HUAWEI TECH Cybersecurity Innovation Lab. The authors also wish to thank the experts from the cooperative company and the cyber security team in our university who helped with the system evaluation.
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Zhang, Y., Liu, H., Dong, X. et al. HyIDSVis: hybrid intrusion detection visualization analysis based on rare category and association rules. J Vis (2021). https://doi.org/10.1007/s12650-021-00789-5
- Visualization system
- Cyber intrusion detection
- Rare category detection
- Association rules